text: #
  # Copyright (C) 2018 The Android Open Source Project
  # Copyright (C) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  '''
  Testing (Reduce)Mean op when the input is dynamic.
      input[1, 3, 4, 1] shape [4]
            |             |
            +-------------+
            |
         Reshape (added by DynamicInputGenerator since it generates its output to be dynamic)
            |
            |        axis = 2    keepDims = True
            |             |             |
            +-------------+-------------+
            |
            |
            | dynamic tensor at compilation time but the shape will be [1, 3, 4, 1] at execution time
            |
        ReduceMean
            |
          output (dynamic tensor, [1, 3, 1, 1] at execution time)
  '''
  
  import dynamic_tensor
  
  model = Model()
  
  model_input_shape = [1, 3, 4, 1]
  
  dynamic_layer = dynamic_tensor.DynamicInputGenerator(model, model_input_shape, "TENSOR_FLOAT32")
  test_node_input = dynamic_layer.getTestNodeInput()
  
  # write REDUCE_MEAN test. input is `test_input`
  
  # note output shape is used by expected output's shape
  model_output = Output("output", "TENSOR_FLOAT32", "{1, 3, 1, 1}")
  
  axis = Int32Scalar("axis", 2)
  keepDims = Int32Scalar("keepDims", 1)
  
  model.Operation("MEAN", test_node_input, axis, keepDims).To(model_output)
  
  model_input_data = [1.0, 2.0, 3.0, 4.0,
                      1.0, 2.0, 3.0, 4.0,
                      1.0, 2.0, 3.0, 4.0]
  
  model_output_data = [2.5, 2.5, 2.5]
  
  Example({
      dynamic_layer.getModelInput() : model_input_data,
      dynamic_layer.getShapeInput() : model_input_shape,
  
      model_output: model_output_data,
  })
  